p3/preprocess/Brugada_Syndrome/code/GSE136992.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Brugada_Syndrome"
cohort = "GSE136992"

# Input paths
in_trait_dir = "../DATA/GEO/Brugada_Syndrome"
in_cohort_dir = "../DATA/GEO/Brugada_Syndrome/GSE136992"

# Output paths
out_data_file = "./output/preprocess/3/Brugada_Syndrome/GSE136992.csv"
out_gene_data_file = "./output/preprocess/3/Brugada_Syndrome/gene_data/GSE136992.csv"
out_clinical_data_file = "./output/preprocess/3/Brugada_Syndrome/clinical_data/GSE136992.csv"
json_path = "./output/preprocess/3/Brugada_Syndrome/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info, this is mRNA expression data from Illumina array
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion
# Trait (Brugada Syndrome):
# From background info: Control vs Infection cases, where Infection cases had downregulation 
# of KCNE5 associated with Brugada syndrome
trait_row = 0  # The condition info is in row 0
def convert_trait(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    # Control = 0, Infection = 1 since infection cases are the ones with Brugada-related gene changes
    if value == 'Control':
        return 0
    elif value == 'Infection':
        return 1
    return None

# Age
age_row = 2  # Age info is in row 2
def convert_age(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'weeks' in value:
        try:
            # Convert weeks to years
            return float(value.replace('weeks', '').strip()) / 52.0
        except:
            return None
    return None

# Gender 
gender_row = 3  # Gender info is in row 3
def convert_gender(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Extract Clinical Features
if trait_row is not None:
    clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row, 
        convert_gender=convert_gender
    )
    
    # Preview the data
    preview = preview_df(clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# The gene identifiers start with "ILMN_" which indicates these are Illumina BeadArray probe IDs
# They need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. Identify columns for mapping:
# 'ID' contains Illumina IDs like those in gene expression data
# 'Symbol' contains gene symbols
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print info about the mapping results
print(f"Original probe data shape: {genetic_data.shape}")
print(f"After mapping to genes shape: {gene_data.shape}")
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data  
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available, 
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)